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README.md CHANGED
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: peft
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+ license: other
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+ base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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+ tags:
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+ - llama-factory
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+ - lora
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+ - unsloth
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+ - generated_from_trainer
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+ model-index:
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+ - name: mistral-finetuned
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # mistral-finetuned
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+
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+ This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the treino_pt_rde dataset.
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 3.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.12.0
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+ - Transformers 4.46.1
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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+ ---
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+ base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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+ library_name: peft
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+ ### Framework versions
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+
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+ - PEFT 0.12.0
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+ "pad_token_id": 770,
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+ "quantization_config": {
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+ "_load_in_4bit": true,
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+ "_load_in_8bit": false,
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+ "bnb_4bit_compute_dtype": "bfloat16",
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+ "bnb_4bit_quant_storage": "uint8",
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+ "bnb_4bit_quant_type": "nf4",
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+ "bnb_4bit_use_double_quant": true,
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+ "llm_int8_enable_fp32_cpu_offload": false,
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+ "llm_int8_has_fp16_weight": false,
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+ "llm_int8_skip_modules": null,
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+ "llm_int8_threshold": 6.0,
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+ "load_in_4bit": true,
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+ "load_in_8bit": false,
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+ "quant_method": "bitsandbytes"
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+ },
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 1000000.0,
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+ "sliding_window": null,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.46.1",
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+ "unsloth_version": "2024.9",
223
+ "use_cache": true,
224
+ "vocab_size": 32768
225
+ }
226
+
227
+
228
+ [INFO|2024-11-25 18:35:58] configuration_utils.py:679 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--unsloth--mistral-7b-instruct-v0.3-bnb-4bit/snapshots/d5f623888f1415cf89b5c208d09cb620694618ee/config.json
229
+
230
+ [INFO|2024-11-25 18:35:58] configuration_utils.py:746 >> Model config MistralConfig {
231
+ "_name_or_path": "unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
232
+ "architectures": [
233
+ "MistralForCausalLM"
234
+ ],
235
+ "attention_dropout": 0.0,
236
+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "head_dim": 128,
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+ "hidden_act": "silu",
240
+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 32768,
244
+ "model_type": "mistral",
245
+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
247
+ "num_key_value_heads": 8,
248
+ "pad_token_id": 770,
249
+ "quantization_config": {
250
+ "_load_in_4bit": true,
251
+ "_load_in_8bit": false,
252
+ "bnb_4bit_compute_dtype": "bfloat16",
253
+ "bnb_4bit_quant_storage": "uint8",
254
+ "bnb_4bit_quant_type": "nf4",
255
+ "bnb_4bit_use_double_quant": true,
256
+ "llm_int8_enable_fp32_cpu_offload": false,
257
+ "llm_int8_has_fp16_weight": false,
258
+ "llm_int8_skip_modules": null,
259
+ "llm_int8_threshold": 6.0,
260
+ "load_in_4bit": true,
261
+ "load_in_8bit": false,
262
+ "quant_method": "bitsandbytes"
263
+ },
264
+ "rms_norm_eps": 1e-05,
265
+ "rope_theta": 1000000.0,
266
+ "sliding_window": null,
267
+ "tie_word_embeddings": false,
268
+ "torch_dtype": "float16",
269
+ "transformers_version": "4.46.1",
270
+ "unsloth_version": "2024.9",
271
+ "use_cache": true,
272
+ "vocab_size": 32768
273
+ }
274
+
275
+
276
+ [INFO|2024-11-25 18:37:37] modeling_utils.py:3937 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--unsloth--mistral-7b-instruct-v0.3-bnb-4bit/snapshots/d5f623888f1415cf89b5c208d09cb620694618ee/model.safetensors
277
+
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+ [INFO|2024-11-25 18:37:37] modeling_utils.py:1670 >> Instantiating MistralForCausalLM model under default dtype torch.float16.
279
+
280
+ [INFO|2024-11-25 18:37:37] configuration_utils.py:1096 >> Generate config GenerationConfig {
281
+ "bos_token_id": 1,
282
+ "eos_token_id": 2,
283
+ "pad_token_id": 770
284
+ }
285
+
286
+
287
+ [INFO|2024-11-25 18:38:02] modeling_utils.py:4800 >> All model checkpoint weights were used when initializing MistralForCausalLM.
288
+
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+
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+ [INFO|2024-11-25 18:38:02] modeling_utils.py:4808 >> All the weights of MistralForCausalLM were initialized from the model checkpoint at unsloth/mistral-7b-instruct-v0.3-bnb-4bit.
291
+ If your task is similar to the task the model of the checkpoint was trained on, you can already use MistralForCausalLM for predictions without further training.
292
+
293
+ [INFO|2024-11-25 18:38:02] configuration_utils.py:1051 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--unsloth--mistral-7b-instruct-v0.3-bnb-4bit/snapshots/d5f623888f1415cf89b5c208d09cb620694618ee/generation_config.json
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+
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+ [INFO|2024-11-25 18:38:02] configuration_utils.py:1096 >> Generate config GenerationConfig {
296
+ "bos_token_id": 1,
297
+ "eos_token_id": 2,
298
+ "max_length": 32768,
299
+ "pad_token_id": 770
300
+ }
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+
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+
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+ [INFO|2024-11-25 18:38:06] logging.py:157 >> Gradient checkpointing enabled.
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+
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+ [INFO|2024-11-25 18:38:06] logging.py:157 >> Upcasting trainable params to float32.
306
+
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+ [INFO|2024-11-25 18:38:06] logging.py:157 >> Fine-tuning method: LoRA
308
+
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+ [INFO|2024-11-25 18:38:06] logging.py:157 >> Found linear modules: gate_proj,up_proj,v_proj,down_proj,o_proj,q_proj,k_proj
310
+
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+ [WARNING|2024-11-25 18:38:10] logging.py:168 >> Unsloth 2024.11.9 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.
312
+
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+ [INFO|2024-11-25 18:38:12] logging.py:157 >> trainable params: 20,971,520 || all params: 7,268,995,072 || trainable%: 0.2885
314
+
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+ [INFO|2024-11-25 18:38:12] trainer.py:698 >> Using auto half precision backend
316
+
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+ [WARNING|2024-11-25 18:38:13] <string>:208 >> ==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1
318
+ \\ /| Num examples = 3,716 | Num Epochs = 3
319
+ O^O/ \_/ \ Batch size per device = 8 | Gradient Accumulation steps = 4
320
+ \ / Total batch size = 32 | Total steps = 348
321
+ "-____-" Number of trainable parameters = 20,971,520
322
+
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+ [INFO|2024-11-25 18:44:44] logging.py:157 >> {'loss': 0.1805, 'learning_rate': 2.9939e-05, 'epoch': 0.09}
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+
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+ [INFO|2024-11-25 18:51:07] logging.py:157 >> {'loss': 0.0827, 'learning_rate': 2.9756e-05, 'epoch': 0.17}
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+
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+ [INFO|2024-11-25 18:57:27] logging.py:157 >> {'loss': 0.0722, 'learning_rate': 2.9453e-05, 'epoch': 0.26}
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+
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+ [INFO|2024-11-25 19:03:51] logging.py:157 >> {'loss': 0.0637, 'learning_rate': 2.9033e-05, 'epoch': 0.34}
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+
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+ [INFO|2024-11-25 19:10:09] logging.py:157 >> {'loss': 0.0646, 'learning_rate': 2.8498e-05, 'epoch': 0.43}
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+
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+ [INFO|2024-11-25 19:16:32] logging.py:157 >> {'loss': 0.0619, 'learning_rate': 2.7853e-05, 'epoch': 0.52}
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+
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+ [INFO|2024-11-25 19:22:55] logging.py:157 >> {'loss': 0.0588, 'learning_rate': 2.7103e-05, 'epoch': 0.60}
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+
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+ [INFO|2024-11-25 19:29:16] logging.py:157 >> {'loss': 0.0594, 'learning_rate': 2.6255e-05, 'epoch': 0.69}
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+
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+ [INFO|2024-11-25 19:35:47] logging.py:157 >> {'loss': 0.0590, 'learning_rate': 2.5315e-05, 'epoch': 0.77}
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+
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+ [INFO|2024-11-25 19:42:12] logging.py:157 >> {'loss': 0.0557, 'learning_rate': 2.4292e-05, 'epoch': 0.86}
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+
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+ [INFO|2024-11-25 19:48:30] logging.py:157 >> {'loss': 0.0493, 'learning_rate': 2.3192e-05, 'epoch': 0.95}
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+
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+ [INFO|2024-11-25 19:54:47] logging.py:157 >> {'loss': 0.0509, 'learning_rate': 2.2026e-05, 'epoch': 1.03}
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+
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+ [INFO|2024-11-25 20:01:14] logging.py:157 >> {'loss': 0.0419, 'learning_rate': 2.0803e-05, 'epoch': 1.12}
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+
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+ [INFO|2024-11-25 20:07:39] logging.py:157 >> {'loss': 0.0420, 'learning_rate': 1.9532e-05, 'epoch': 1.20}
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+
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+ [INFO|2024-11-25 20:13:57] logging.py:157 >> {'loss': 0.0432, 'learning_rate': 1.8225e-05, 'epoch': 1.29}
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+
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+
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+
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+
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+ [INFO|2024-11-25 20:45:43] logging.py:157 >> {'loss': 0.0401, 'learning_rate': 1.1511e-05, 'epoch': 1.72}
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+
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+
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+
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+ [INFO|2024-11-25 21:04:51] logging.py:157 >> {'loss': 0.0410, 'learning_rate': 7.7358e-06, 'epoch': 1.98}
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+
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+ [INFO|2024-11-25 21:11:09] logging.py:157 >> {'loss': 0.0317, 'learning_rate': 6.5822e-06, 'epoch': 2.06}
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+
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+
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+
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+
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+
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+
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+ [INFO|2024-11-25 21:49:30] logging.py:157 >> {'loss': 0.0279, 'learning_rate': 1.3864e-06, 'epoch': 2.58}
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+
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+ [INFO|2024-11-25 21:55:50] logging.py:157 >> {'loss': 0.0321, 'learning_rate': 8.7399e-07, 'epoch': 2.67}
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+
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+ [INFO|2024-11-25 22:02:10] logging.py:157 >> {'loss': 0.0352, 'learning_rate': 4.7666e-07, 'epoch': 2.75}
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+
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+ [INFO|2024-11-25 22:08:34] logging.py:157 >> {'loss': 0.0341, 'learning_rate': 1.9760e-07, 'epoch': 2.84}
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+
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+ [INFO|2024-11-25 22:14:53] logging.py:157 >> {'loss': 0.0315, 'learning_rate': 3.9102e-08, 'epoch': 2.92}
390
+
391
+ [INFO|2024-11-25 22:19:59] trainer.py:3801 >> Saving model checkpoint to saves/Mistral-7B-Instruct-v0.3/lora/mistral-finetuned/checkpoint-348
392
+
393
+ [INFO|2024-11-25 22:19:59] configuration_utils.py:679 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--unsloth--mistral-7b-instruct-v0.3-bnb-4bit/snapshots/d5f623888f1415cf89b5c208d09cb620694618ee/config.json
394
+
395
+ [INFO|2024-11-25 22:19:59] configuration_utils.py:746 >> Model config MistralConfig {
396
+ "_name_or_path": "unsloth/Mistral-7B-Instruct-v0.3",
397
+ "architectures": [
398
+ "MistralForCausalLM"
399
+ ],
400
+ "attention_dropout": 0.0,
401
+ "bos_token_id": 1,
402
+ "eos_token_id": 2,
403
+ "head_dim": 128,
404
+ "hidden_act": "silu",
405
+ "hidden_size": 4096,
406
+ "initializer_range": 0.02,
407
+ "intermediate_size": 14336,
408
+ "max_position_embeddings": 32768,
409
+ "model_type": "mistral",
410
+ "num_attention_heads": 32,
411
+ "num_hidden_layers": 32,
412
+ "num_key_value_heads": 8,
413
+ "pad_token_id": 770,
414
+ "quantization_config": {
415
+ "_load_in_4bit": true,
416
+ "_load_in_8bit": false,
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+ "bnb_4bit_compute_dtype": "bfloat16",
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+ "bnb_4bit_quant_storage": "uint8",
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+ "bnb_4bit_quant_type": "nf4",
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+ "bnb_4bit_use_double_quant": true,
421
+ "llm_int8_enable_fp32_cpu_offload": false,
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+ "llm_int8_has_fp16_weight": false,
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+ "llm_int8_skip_modules": null,
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+ "llm_int8_threshold": 6.0,
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+ "load_in_4bit": true,
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+ "load_in_8bit": false,
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+ "quant_method": "bitsandbytes"
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+ },
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 1000000.0,
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+ "sliding_window": null,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.46.1",
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+ "unsloth_version": "2024.9",
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+ "use_cache": true,
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+ "vocab_size": 32768
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+ }
439
+
440
+
441
+ [INFO|2024-11-25 22:20:00] <string>:484 >>
442
+
443
+ Training completed. Do not forget to share your model on huggingface.co/models =)
444
+
445
+
446
+
447
+ [INFO|2024-11-25 22:20:00] trainer.py:3801 >> Saving model checkpoint to saves/Mistral-7B-Instruct-v0.3/lora/mistral-finetuned
448
+
449
+ [INFO|2024-11-25 22:20:00] configuration_utils.py:679 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--unsloth--mistral-7b-instruct-v0.3-bnb-4bit/snapshots/d5f623888f1415cf89b5c208d09cb620694618ee/config.json
450
+
451
+ [INFO|2024-11-25 22:20:00] configuration_utils.py:746 >> Model config MistralConfig {
452
+ "_name_or_path": "unsloth/Mistral-7B-Instruct-v0.3",
453
+ "architectures": [
454
+ "MistralForCausalLM"
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+ ],
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "head_dim": 128,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 32768,
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+ "model_type": "mistral",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "pad_token_id": 770,
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+ "quantization_config": {
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+ "_load_in_4bit": true,
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+ "_load_in_8bit": false,
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+ "bnb_4bit_compute_dtype": "bfloat16",
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+ "bnb_4bit_quant_storage": "uint8",
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+ "bnb_4bit_quant_type": "nf4",
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 1000000.0,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "unsloth_version": "2024.9",
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+ }
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+
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+
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+ [WARNING|2024-11-25 22:20:05] logging.py:162 >> No metric eval_loss to plot.
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+
499
+ [WARNING|2024-11-25 22:20:05] logging.py:162 >> No metric eval_accuracy to plot.
500
+
501
+ [INFO|2024-11-25 22:20:05] modelcard.py:449 >> Dropping the following result as it does not have all the necessary fields:
502
+ {'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
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+
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